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Journal of Data Science


Common Growth Patterns for Regional Social Networks: A Point Process Approach
Volume 21, Issue 3 (2023): Special Issue: Advances in Network Data Science, pp. 446–469
Pub. online: 16 September 2021      Type: Statistical Data Science      Open accessOpen Access
Received
19 April 2021
Accepted
18 August 2021
Published
16 September 2021

Abstract

In this paper, we study macroscopic growth dynamics of social network link formation. Rather than focusing on one particular dataset, we find invariant behavior in regional social networks that are geographically concentrated. Empirical findings suggest that the startup phase of a regional network can be modeled by a self-exciting point process. After the startup phase ends, the growth of the links can be modeled by a non-homogeneous Poisson process with a constant rate across the day but varying rates from day to day, plus a nightly inactive period when local users are expected to be asleep. Conclusions are drawn based on analyzing four different datasets, three of which are regional and a non-regional one is included for contrast.

Supplementary material

 Supplementary Material
We have collected all relevant codes and datasets, and made them available at https://github.com/tw398/NetworkGrowth. This GitHub repository includes: (1) links to the corresponding dataset on KONECT; (2) cleaned Dutch Wikipedia talk data; (3) all relevant codes for data analyses in the paper; and (4) ACF plots which are not presented in the paper.

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Copyright
2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
estimation network growth non-homogeneous Poisson processes point processes self-exciting processes

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